## No trace type specified:
##   Based on info supplied, a 'scatter' trace seems appropriate.
##   Read more about this trace type -> https://plot.ly/r/reference/#scatter
## No trace type specified:
##   Based on info supplied, a 'bar' trace seems appropriate.
##   Read more about this trace type -> https://plot.ly/r/reference/#bar

Analysis

nyc.avg.rate <- nyc.covid19.mask %>% 
  select("zip", "date","area", "COVID_CASE_RATE") %>% 
  dplyr::group_by(zip) %>% 
  dplyr::arrange(zip, date) %>% 
  mutate(rate = (COVID_CASE_RATE - lag(COVID_CASE_RATE))/lag(COVID_CASE_RATE)) %>% 
  summarise(avg_rate = mean(rate, na.rm = TRUE))
## `summarise()` ungrouping output (override with `.groups` argument)
nyc.avg.rate.mask <- left_join(nyt.data, nyc.avg.rate, by ="zip") %>% 
  mutate(obs_mask = 1 - obs_mask)
plot_ly(nyc.avg.rate.mask,
        x = ~area,
        y = ~obs_mask,
        type = 'scatter',
        mode = 'markers',
        name = 'Mask Rate',
        visible = T) %>% 
  add_trace(nyc.avg.rate.mask, y = ~avg_rate, name = 'Avg. Change Rate', visible = T) %>% 
  layout(
    title = 'Observed Mask Rates & COVID 19 Positive Rates by Area',
    showlegend = TRUE,
    yaxis = list(title = "% of Masks Observed/COVID 19 Positive Rate",tickformat = "%"),
    xaxis = list(title = "Area"),
    hovermode = 'compare'
)